Python, Data Science & Machine Learning Integrated - Hybrid Project

Overview

This repository contains the project completed as part of the "Python, Data Science & Machine Learning Integrated - Hybrid" summer course. The course covered a comprehensive curriculum, integrating Python programming with essential data science techniques and machine learning models.

Project Details

Project Title: Mall Customer Segmentation

Objective

The primary objective of this project is to analyze customer data from a mall and segment the customers into distinct groups using various data science and machine learning techniques.

Features and Functionalities

• Data Collection and Preprocessing: The project starts with data collection from Kaggle. The data is then cleaned and preprocessed to handle missing values, outliers, and other inconsistencies.

• Exploratory Data Analysis (EDA): Detailed exploratory data analysis is performed to understand the data better. This includes:

  • Summary statistics
  • Data visualization using libraries like Matplotlib and Seaborn
  • Identifying trends, patterns, and correlations within the data

• Data Visualization: Various graphs and plots are created to visualize the data, including:

  • Histograms
  • Scatter plots
  • Line graphs
  • Box plots
  • Heatmaps

• Feature Engineering: Important features are selected and engineered to improve the performance of the machine learning models.

• Machine Learning Models: The project implements several machine learning models to achieve its objectives. This includes:

  • Linear Regression
  • Decision Trees
  • Random Forest
  • Support Vector Machines (SVM)
  • Neural Networks

• Model Evaluation: The performance of the models is evaluated using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. Hyperparameter tuning and cross-validation are also conducted to optimize the models.

• Predictions and Insights: Based on the trained models, predictions are made, and insights are drawn to help understand the underlying patterns in the data.

Technologies Used

• Programming Language: Python
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, TensorFlow/Keras
• Tools: Jupyter Notebook, Google Colab

Installation and Usage

Clone the Repository

git clone https://github.com/[your-github-username]/[your-repo-name].git
cd [your-repo-name]

Install Dependencies

Ensure you have Python installed. You can create a virtual environment and install the necessary dependencies using the following commands:

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
pip install -r requirements.txt

Running the Project

1. Open the Jupyter Notebook or Google Colab file.

jupyter notebook your_notebook.ipynb

2. Execute the cells to run the analysis and visualize the data.

Usage

Follow the instructions provided in the notebook to run the various sections of the project, including data preprocessing, EDA, model training, and evaluation.

Links

• Project Notebook on Google Colab

Contributing

Contributions are welcome! Please fork this repository and submit a pull request for any improvements, bug fixes, or new features.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

Special thanks to the course instructors and fellow students for their guidance and support throughout this project.

Future Work

• Expand the Dataset: Use larger and more diverse datasets to enhance the robustness of the models.
• Advanced Models: Explore advanced machine learning techniques such as deep learning.
• Deployment: Deploy the models using web frameworks to make them accessible to a wider audience.